1. Field of the Invention
The present invention is a system, method, and apparatus for determining the presence of a crowd and its impact on retail performance, using video analytics-based measurement of behavior patterns of people in a store area, where a crowd index measures the level of crowding in a store area, which can be correlated with the changes in behavior of people and changes in sales.
2. Background of the Invention
An earlier attempt for crowd detection can be found in U.S. Pat. No. 4,924,416 of Sasao (hereinafter Sasao). Sasao disclosed an apparatus for detecting the degree of crowding in an elevator hall, using a television camera and illumination controller. The application area of the apparatus is related to an elevator hall that is very different from a retail space. Therefore, Sasao was entirely foreign to the idea of measuring the crowd impact on shopper behavior. Furthermore, Sasao counted brightness difference between a hall video data and a reference data stored in a data memory to calculate the degree of crowding. Sasao is completely different from the ideas and methods of crowd detection disclosed in the present invention. In an exemplary embodiment, the present invention applies spatiotemporal criteria to shoppers' trips to detect a crowd and calculate a crowd index in a store area in a retail space. The shoppers' trips are detected based on tracking information by video analytics of the video images captured in the target measurement area.
U.S. Pat. No. 6,987,885 of Gonzalez-Banos, et al. (hereinafter Gonzalez-Banos) disclosed a system and method to determine the number of people in a crowd, using visual hulls. Gonzalez-Banos is based on aggregated planar projections of the intersections of a silhouette image cone and a working volume. Gonzalez-Banos is entirely foreign to the idea of using image-based tracking to detect shoppers' trips in a retail store and detect a crowd based on the tracking of people. Furthermore, Gonzalez-Banos is entirely foreign to the idea of determining the impact of crowding on retail performance, as disclosed in the present invention.
U.S. Pat. No. 7,139,409 of Paragios, et al. and U.S. Pat. No. 7,457,436 of Paragios, et al. (hereinafter Paragios) disclosed a system and method for crowd density estimation in a subway environment. Paragios used a change detection algorithm to distinguish a background scene from a foreground, and Paragios combined the change detection map with geometric weights to estimate a measure of congestion of the subway platform. Paragios used the total weighted sum over the segmented region as the calculated crowdedness measure. Paragios' method is specific to a subway platform application. Paragios is also foreign to the idea of using image-based tracking to detect shoppers' trips in a retail store and detect a crowd based on the tracking of people. Furthermore, Paragios is entirely foreign to the idea of determining the impact of crowding on retail performance, as disclosed in the present invention.
U.S. Pat. No. 6,633,232 of Trajkovic, et al. and U.S. Pat. Appl. Pub. No. 20020168084 of Trajkovic, et al. (hereinafter Trajkovic) disclosed a vision system for computing crowd density. Trajkovic suggests some methods of crowd prediction in a general approach. For example, Trajkovic suggests an image compression process as a surrogate for prediction of crowd density. Trajkovic also suggests historical information, external data, and probabilistic techniques as methods to predict the crowd. Although Trajkovic further suggests usage of a classification engine that is programmed to distinguish masses of individuals, where the classification engine identifies the locations and motion vectors of each individual, Trajkovic is entirely foreign to the idea of using image-based tracking to detect shoppers' trips in a retail store and to detect a crowd based on the tracking of people, as discussed in the present invention. In an exemplary embodiment, the present invention applies spatiotemporal criteria to shoppers' trips to detect a crowd, not just to predict, and the present invention calculates a crowd index based on the crowd detection in a store area in a retail space. Furthermore, Trajkovic is entirely foreign to the idea of determining the impact of crowding on retail performance, as disclosed in the present invention.